Overview

Dataset statistics

Number of variables20
Number of observations42
Missing cells171
Missing cells (%)20.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory156.0 B

Variable types

Numeric17
DateTime1
Categorical1
Boolean1

Alerts

df_index is highly correlated with OnsetHigh correlation
Onset is highly correlated with df_index and 5 other fieldsHigh correlation
Offset is highly correlated with MidpointHigh correlation
TST is highly correlated with Onset and 5 other fieldsHigh correlation
WASO is highly correlated with TIB and 2 other fieldsHigh correlation
NOA is highly correlated with Onset and 7 other fieldsHigh correlation
TIB is highly correlated with Onset and 6 other fieldsHigh correlation
REMSD is highly correlated with TST and 6 other fieldsHigh correlation
LSD is highly correlated with Onset and 6 other fieldsHigh correlation
DSD is highly correlated with SWSPHigh correlation
TSDP is highly correlated with Onset and 6 other fieldsHigh correlation
AI is highly correlated with NOAHigh correlation
SWSP is highly correlated with NOA and 3 other fieldsHigh correlation
REMP is highly correlated with REMSDHigh correlation
SMI is highly correlated with WASOHigh correlation
Midpoint is highly correlated with OffsetHigh correlation
df_index is highly correlated with OnsetHigh correlation
Onset is highly correlated with df_index and 6 other fieldsHigh correlation
Offset is highly correlated with MidpointHigh correlation
TST is highly correlated with Onset and 6 other fieldsHigh correlation
WASO is highly correlated with TIB and 2 other fieldsHigh correlation
NOA is highly correlated with Onset and 7 other fieldsHigh correlation
TIB is highly correlated with Onset and 6 other fieldsHigh correlation
REMSD is highly correlated with Onset and 7 other fieldsHigh correlation
LSD is highly correlated with Onset and 6 other fieldsHigh correlation
DSD is highly correlated with SWSPHigh correlation
TSDP is highly correlated with Onset and 6 other fieldsHigh correlation
AI is highly correlated with NOAHigh correlation
SWSP is highly correlated with TST and 4 other fieldsHigh correlation
REMP is highly correlated with REMSDHigh correlation
SMI is highly correlated with WASOHigh correlation
Midpoint is highly correlated with OffsetHigh correlation
Onset is highly correlated with TST and 3 other fieldsHigh correlation
Offset is highly correlated with MidpointHigh correlation
TST is highly correlated with Onset and 5 other fieldsHigh correlation
WASO is highly correlated with SMIHigh correlation
NOA is highly correlated with TST and 3 other fieldsHigh correlation
TIB is highly correlated with Onset and 4 other fieldsHigh correlation
REMSD is highly correlated with TST and 1 other fieldsHigh correlation
LSD is highly correlated with Onset and 4 other fieldsHigh correlation
TSDP is highly correlated with Onset and 4 other fieldsHigh correlation
REMP is highly correlated with REMSDHigh correlation
SMI is highly correlated with WASOHigh correlation
Midpoint is highly correlated with OffsetHigh correlation
IsWeekend is highly correlated with DayHigh correlation
Day is highly correlated with IsWeekendHigh correlation
df_index is highly correlated with Onset and 5 other fieldsHigh correlation
Onset is highly correlated with df_index and 8 other fieldsHigh correlation
Offset is highly correlated with Onset and 6 other fieldsHigh correlation
TST is highly correlated with df_index and 7 other fieldsHigh correlation
WASO is highly correlated with Onset and 7 other fieldsHigh correlation
NOA is highly correlated with TIB and 7 other fieldsHigh correlation
TIB is highly correlated with Onset and 10 other fieldsHigh correlation
REMSD is highly correlated with Offset and 8 other fieldsHigh correlation
LSD is highly correlated with Onset and 7 other fieldsHigh correlation
DSD is highly correlated with WASO and 6 other fieldsHigh correlation
Date is highly correlated with df_index and 18 other fieldsHigh correlation
TSDP is highly correlated with Onset and 10 other fieldsHigh correlation
AI is highly correlated with df_index and 1 other fieldsHigh correlation
SWSP is highly correlated with df_index and 4 other fieldsHigh correlation
REMP is highly correlated with NOA and 7 other fieldsHigh correlation
SMI is highly correlated with Offset and 3 other fieldsHigh correlation
Midpoint is highly correlated with Onset and 11 other fieldsHigh correlation
Day is highly correlated with Date and 2 other fieldsHigh correlation
IsWeekend is highly correlated with Date and 2 other fieldsHigh correlation
SleepRegularity is highly correlated with df_index and 3 other fieldsHigh correlation
Onset has 11 (26.2%) missing values Missing
Offset has 11 (26.2%) missing values Missing
TST has 11 (26.2%) missing values Missing
WASO has 11 (26.2%) missing values Missing
NOA has 11 (26.2%) missing values Missing
TIB has 11 (26.2%) missing values Missing
REMSD has 11 (26.2%) missing values Missing
LSD has 11 (26.2%) missing values Missing
DSD has 11 (26.2%) missing values Missing
TSDP has 11 (26.2%) missing values Missing
AI has 11 (26.2%) missing values Missing
SWSP has 11 (26.2%) missing values Missing
REMP has 11 (26.2%) missing values Missing
SMI has 11 (26.2%) missing values Missing
Midpoint has 11 (26.2%) missing values Missing
SleepRegularity has 6 (14.3%) missing values Missing
df_index is uniformly distributed Uniform
REMP is uniformly distributed Uniform
Day is uniformly distributed Uniform
df_index has unique values Unique
Date has unique values Unique
df_index has 1 (2.4%) zeros Zeros

Reproduction

Analysis started2022-11-25 20:45:48.019959
Analysis finished2022-11-25 20:46:40.774118
Duration52.75 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE
ZEROS

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.5
Minimum0
Maximum41
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:40.928122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.05
Q110.25
median20.5
Q330.75
95-th percentile38.95
Maximum41
Range41
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation12.26784415
Coefficient of variation (CV)0.5984314218
Kurtosis-1.2
Mean20.5
Median Absolute Deviation (MAD)10.5
Skewness0
Sum861
Variance150.5
MonotonicityNot monotonic
2022-11-25T20:46:41.104120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
01
 
2.4%
211
 
2.4%
141
 
2.4%
161
 
2.4%
151
 
2.4%
181
 
2.4%
171
 
2.4%
201
 
2.4%
191
 
2.4%
221
 
2.4%
Other values (32)32
76.2%
ValueCountFrequency (%)
01
2.4%
11
2.4%
21
2.4%
31
2.4%
41
2.4%
51
2.4%
61
2.4%
71
2.4%
81
2.4%
91
2.4%
ValueCountFrequency (%)
411
2.4%
401
2.4%
391
2.4%
381
2.4%
371
2.4%
361
2.4%
351
2.4%
341
2.4%
331
2.4%
321
2.4%

Onset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)96.8%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean23.9483871
Minimum22.41666667
Maximum27.06666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:41.270123image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum22.41666667
5-th percentile22.49166667
Q123.11666667
median23.75
Q324.46666667
95-th percentile26.26666667
Maximum27.06666667
Range4.65
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.21299564
Coefficient of variation (CV)0.05065041062
Kurtosis0.4608349857
Mean23.9483871
Median Absolute Deviation (MAD)0.6666666667
Skewness0.958656775
Sum742.4
Variance1.471358423
MonotonicityNot monotonic
2022-11-25T20:46:41.427119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
23.752
 
4.8%
23.183333331
 
2.4%
23.21
 
2.4%
24.051
 
2.4%
24.366666671
 
2.4%
261
 
2.4%
23.433333331
 
2.4%
24.566666671
 
2.4%
23.716666671
 
2.4%
23.083333331
 
2.4%
Other values (20)20
47.6%
(Missing)11
26.2%
ValueCountFrequency (%)
22.416666671
2.4%
22.433333331
2.4%
22.551
2.4%
22.566666671
2.4%
22.751
2.4%
22.766666671
2.4%
22.783333331
2.4%
23.083333331
2.4%
23.151
2.4%
23.183333331
2.4%
ValueCountFrequency (%)
27.066666671
2.4%
26.433333331
2.4%
26.11
2.4%
261
2.4%
25.21
2.4%
24.91
2.4%
24.583333331
2.4%
24.566666671
2.4%
24.366666671
2.4%
24.216666671
2.4%

Offset
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)83.9%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean7.473655914
Minimum5.65
Maximum8.933333333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:41.583117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum5.65
5-th percentile6.65
Q17.141666667
median7.383333333
Q37.8
95-th percentile8.575
Maximum8.933333333
Range3.283333333
Interquartile range (IQR)0.6583333333

Descriptive statistics

Standard deviation0.6355192423
Coefficient of variation (CV)0.08503458676
Kurtosis1.818242839
Mean7.473655914
Median Absolute Deviation (MAD)0.2833333333
Skewness-0.139277355
Sum231.6833333
Variance0.4038847073
MonotonicityNot monotonic
2022-11-25T20:46:41.733150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7.7333333332
 
4.8%
7.2833333332
 
4.8%
7.2166666672
 
4.8%
7.1333333332
 
4.8%
7.6666666672
 
4.8%
7.5333333331
 
2.4%
7.3833333331
 
2.4%
7.9333333331
 
2.4%
8.751
 
2.4%
7.451
 
2.4%
Other values (16)16
38.1%
(Missing)11
26.2%
ValueCountFrequency (%)
5.651
2.4%
6.5833333331
2.4%
6.7166666671
2.4%
6.8666666671
2.4%
7.0333333331
2.4%
7.1166666671
2.4%
7.1333333332
4.8%
7.151
2.4%
7.2166666672
4.8%
7.251
2.4%
ValueCountFrequency (%)
8.9333333331
2.4%
8.751
2.4%
8.41
2.4%
8.1833333331
2.4%
7.951
2.4%
7.9333333331
2.4%
7.8833333331
2.4%
7.8666666671
2.4%
7.7333333332
4.8%
7.6666666672
4.8%

TST
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)83.9%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean386.3548387
Minimum230
Maximum504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:41.904159image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum230
5-th percentile273.5
Q1352
median395
Q3429
95-th percentile480
Maximum504
Range274
Interquartile range (IQR)77

Descriptive statistics

Standard deviation65.57771389
Coefficient of variation (CV)0.1697344185
Kurtosis-0.01972626742
Mean386.3548387
Median Absolute Deviation (MAD)42
Skewness-0.5288128151
Sum11977
Variance4300.436559
MonotonicityNot monotonic
2022-11-25T20:46:42.044161image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4263
 
7.1%
3522
 
4.8%
3452
 
4.8%
4802
 
4.8%
4471
 
2.4%
2301
 
2.4%
3751
 
2.4%
3731
 
2.4%
2951
 
2.4%
4121
 
2.4%
Other values (16)16
38.1%
(Missing)11
26.2%
ValueCountFrequency (%)
2301
2.4%
2671
2.4%
2801
2.4%
2901
2.4%
2951
2.4%
3452
4.8%
3522
4.8%
3661
2.4%
3691
2.4%
3731
2.4%
ValueCountFrequency (%)
5041
 
2.4%
4802
4.8%
4511
 
2.4%
4471
 
2.4%
4431
 
2.4%
4371
 
2.4%
4301
 
2.4%
4281
 
2.4%
4263
7.1%
4121
 
2.4%

WASO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)77.4%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean65.09677419
Minimum27
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:42.219125image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile40.5
Q154
median59
Q374.5
95-th percentile95
Maximum151
Range124
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation22.64413808
Coefficient of variation (CV)0.3478534591
Kurtosis5.978490196
Mean65.09677419
Median Absolute Deviation (MAD)10
Skewness1.862411967
Sum2018
Variance512.7569892
MonotonicityNot monotonic
2022-11-25T20:46:42.396123image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
483
 
7.1%
542
 
4.8%
552
 
4.8%
612
 
4.8%
572
 
4.8%
662
 
4.8%
501
 
2.4%
591
 
2.4%
811
 
2.4%
761
 
2.4%
Other values (14)14
33.3%
(Missing)11
26.2%
ValueCountFrequency (%)
271
 
2.4%
371
 
2.4%
441
 
2.4%
483
7.1%
501
 
2.4%
542
4.8%
552
4.8%
561
 
2.4%
572
4.8%
581
 
2.4%
ValueCountFrequency (%)
1511
2.4%
971
2.4%
931
2.4%
911
2.4%
821
2.4%
811
2.4%
801
2.4%
761
2.4%
731
2.4%
691
2.4%

NOA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)51.6%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean27.38709677
Minimum16
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:42.543152image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile17.5
Q124
median28
Q331.5
95-th percentile36.5
Maximum40
Range24
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation5.748492088
Coefficient of variation (CV)0.2098978265
Kurtosis-0.1147956868
Mean27.38709677
Median Absolute Deviation (MAD)4
Skewness-0.03508615705
Sum849
Variance33.04516129
MonotonicityNot monotonic
2022-11-25T20:46:42.684122image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
284
 
9.5%
253
 
7.1%
273
 
7.1%
323
 
7.1%
243
 
7.1%
332
 
4.8%
202
 
4.8%
292
 
4.8%
312
 
4.8%
181
 
2.4%
Other values (6)6
14.3%
(Missing)11
26.2%
ValueCountFrequency (%)
161
 
2.4%
171
 
2.4%
181
 
2.4%
202
4.8%
231
 
2.4%
243
7.1%
253
7.1%
273
7.1%
284
9.5%
292
4.8%
ValueCountFrequency (%)
401
 
2.4%
371
 
2.4%
361
 
2.4%
332
4.8%
323
7.1%
312
4.8%
292
4.8%
284
9.5%
273
7.1%
253
7.1%

TIB
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)90.3%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean451.4516129
Minimum267
Maximum580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:42.836123image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum267
5-th percentile309
Q1422
median456
Q3505.5
95-th percentile537
Maximum580
Range313
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation75.03680817
Coefficient of variation (CV)0.1662122939
Kurtosis0.1867628965
Mean451.4516129
Median Absolute Deviation (MAD)47
Skewness-0.74623752
Sum13995
Variance5630.522581
MonotonicityNot monotonic
2022-11-25T20:46:42.982118image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5232
 
4.8%
5032
 
4.8%
5282
 
4.8%
5011
 
2.4%
5461
 
2.4%
4251
 
2.4%
4211
 
2.4%
3561
 
2.4%
4711
 
2.4%
4261
 
2.4%
Other values (18)18
42.9%
(Missing)11
26.2%
ValueCountFrequency (%)
2671
2.4%
3071
2.4%
3111
2.4%
3461
2.4%
3561
2.4%
4001
2.4%
4031
2.4%
4211
2.4%
4231
2.4%
4251
2.4%
ValueCountFrequency (%)
5801
2.4%
5461
2.4%
5282
4.8%
5232
4.8%
5211
2.4%
5081
2.4%
5032
4.8%
5011
2.4%
4871
2.4%
4811
2.4%

REMSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)87.1%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean71.16129032
Minimum36
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:43.154118image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile43.5
Q155
median74
Q384
95-th percentile100
Maximum104
Range68
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.52943024
Coefficient of variation (CV)0.2603863723
Kurtosis-0.9446525894
Mean71.16129032
Median Absolute Deviation (MAD)14
Skewness-0.07627891107
Sum2206
Variance343.3397849
MonotonicityNot monotonic
2022-11-25T20:46:43.306119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
802
 
4.8%
742
 
4.8%
842
 
4.8%
832
 
4.8%
941
 
2.4%
671
 
2.4%
881
 
2.4%
431
 
2.4%
441
 
2.4%
981
 
2.4%
Other values (17)17
40.5%
(Missing)11
26.2%
ValueCountFrequency (%)
361
2.4%
431
2.4%
441
2.4%
471
2.4%
501
2.4%
521
2.4%
531
2.4%
541
2.4%
561
2.4%
601
2.4%
ValueCountFrequency (%)
1041
2.4%
1021
2.4%
981
2.4%
941
2.4%
901
2.4%
881
2.4%
871
2.4%
842
4.8%
832
4.8%
802
4.8%

LSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean235.0645161
Minimum127
Maximum338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:43.475117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum127
5-th percentile154
Q1215.5
median232
Q3263.5
95-th percentile318
Maximum338
Range211
Interquartile range (IQR)48

Descriptive statistics

Standard deviation48.9359687
Coefficient of variation (CV)0.2081810113
Kurtosis0.1524096364
Mean235.0645161
Median Absolute Deviation (MAD)28
Skewness-0.05897798695
Sum7287
Variance2394.729032
MonotonicityNot monotonic
2022-11-25T20:46:43.636151image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2771
 
2.4%
2741
 
2.4%
2181
 
2.4%
2171
 
2.4%
1801
 
2.4%
2601
 
2.4%
2161
 
2.4%
2631
 
2.4%
3231
 
2.4%
2531
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
1271
2.4%
1471
2.4%
1611
2.4%
1691
2.4%
1801
2.4%
1981
2.4%
2081
2.4%
2151
2.4%
2161
2.4%
2171
2.4%
ValueCountFrequency (%)
3381
2.4%
3231
2.4%
3131
2.4%
2971
2.4%
2771
2.4%
2741
2.4%
2661
2.4%
2641
2.4%
2631
2.4%
2601
2.4%

DSD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)80.6%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean80.12903226
Minimum56
Maximum116
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:43.797120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile57
Q168
median81
Q389.5
95-th percentile105
Maximum116
Range60
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation15.71356513
Coefficient of variation (CV)0.1961032685
Kurtosis-0.473989725
Mean80.12903226
Median Absolute Deviation (MAD)11
Skewness0.3644328342
Sum2484
Variance246.916129
MonotonicityNot monotonic
2022-11-25T20:46:43.937119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
902
 
4.8%
722
 
4.8%
1022
 
4.8%
682
 
4.8%
832
 
4.8%
562
 
4.8%
861
 
2.4%
851
 
2.4%
651
 
2.4%
971
 
2.4%
Other values (15)15
35.7%
(Missing)11
26.2%
ValueCountFrequency (%)
562
4.8%
581
2.4%
601
2.4%
631
2.4%
651
2.4%
671
2.4%
682
4.8%
701
2.4%
722
4.8%
741
2.4%
ValueCountFrequency (%)
1161
2.4%
1081
2.4%
1022
4.8%
991
2.4%
971
2.4%
902
4.8%
891
2.4%
871
2.4%
861
2.4%
851
2.4%

Date
Date

HIGH CORRELATION
UNIQUE

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size464.0 B
Minimum2022-09-30 00:00:00
Maximum2022-11-10 00:00:00
2022-11-25T20:46:44.117144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:44.299120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)

TSDP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct29
Distinct (%)93.5%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean451.516129
Minimum267
Maximum580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:44.480139image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum267
5-th percentile309.5
Q1422
median456
Q3505.5
95-th percentile537
Maximum580
Range313
Interquartile range (IQR)83.5

Descriptive statistics

Standard deviation74.97504961
Coefficient of variation (CV)0.1660517638
Kurtosis0.1871463116
Mean451.516129
Median Absolute Deviation (MAD)47
Skewness-0.7458744748
Sum13997
Variance5621.258065
MonotonicityNot monotonic
2022-11-25T20:46:44.947166image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5032
 
4.8%
5282
 
4.8%
5011
 
2.4%
2671
 
2.4%
4251
 
2.4%
4211
 
2.4%
3561
 
2.4%
4711
 
2.4%
4261
 
2.4%
4811
 
2.4%
Other values (19)19
45.2%
(Missing)11
26.2%
ValueCountFrequency (%)
2671
2.4%
3071
2.4%
3121
2.4%
3461
2.4%
3561
2.4%
4001
2.4%
4031
2.4%
4211
2.4%
4231
2.4%
4251
2.4%
ValueCountFrequency (%)
5801
2.4%
5461
2.4%
5282
4.8%
5231
2.4%
5231
2.4%
5211
2.4%
5081
2.4%
5032
4.8%
5011
2.4%
4871
2.4%

AI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean4.253651336
Minimum3.409090909
Maximum5.77540107
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:45.125151image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.409090909
5-th percentile3.496835443
Q13.935713604
median4.257206208
Q34.569781553
95-th percentile5.063628786
Maximum5.77540107
Range2.36631016
Interquartile range (IQR)0.634067949

Descriptive statistics

Standard deviation0.5356622112
Coefficient of variation (CV)0.1259299761
Kurtosis0.9140143182
Mean4.253651336
Median Absolute Deviation (MAD)0.3294409714
Skewness0.6112650838
Sum131.8631914
Variance0.2869340045
MonotonicityNot monotonic
2022-11-25T20:46:45.289126image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4.4295302011
 
2.4%
4.2572062081
 
2.4%
3.841
 
2.4%
4.5040214481
 
2.4%
4.8813559321
 
2.4%
4.5145631071
 
2.4%
4.6956521741
 
2.4%
4.6478873241
 
2.4%
4.7619047621
 
2.4%
4.6969696971
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
3.4090909091
2.4%
3.4936708861
2.4%
3.51
2.4%
3.5046728971
2.4%
3.5172413791
2.4%
3.767441861
2.4%
3.841
2.4%
3.9277652371
2.4%
3.9436619721
2.4%
4.0399002491
2.4%
ValueCountFrequency (%)
5.775401071
2.4%
5.2459016391
2.4%
4.8813559321
2.4%
4.7619047621
2.4%
4.6969696971
2.4%
4.6956521741
2.4%
4.6478873241
2.4%
4.6251
2.4%
4.5145631071
2.4%
4.5040214481
2.4%

SWSP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean21.10505649
Minimum12.81464531
Maximum29.13043478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:45.447119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12.81464531
5-th percentile14.38326931
Q118.24571628
median20.69767442
Q324.67679513
95-th percentile26.50718494
Maximum29.13043478
Range16.31578947
Interquartile range (IQR)6.431078841

Descriptive statistics

Standard deviation4.218950904
Coefficient of variation (CV)0.1999023744
Kurtosis-0.8304308748
Mean21.10505649
Median Absolute Deviation (MAD)3.709105242
Skewness-0.1557541314
Sum654.2567511
Variance17.79954673
MonotonicityNot monotonic
2022-11-25T20:46:45.609159image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20.134228191
 
2.4%
18.403547671
 
2.4%
241
 
2.4%
18.2305631
 
2.4%
24.406779661
 
2.4%
17.475728161
 
2.4%
24.637681161
 
2.4%
15.258215961
 
2.4%
19.246031751
 
2.4%
15.151515151
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
12.814645311
2.4%
13.615023471
2.4%
15.151515151
2.4%
15.258215961
2.4%
16.666666671
2.4%
16.8751
2.4%
17.475728161
2.4%
18.2305631
2.4%
18.260869571
2.4%
18.403547671
2.4%
ValueCountFrequency (%)
29.130434781
2.4%
26.829268291
2.4%
26.185101581
2.4%
25.822784811
2.4%
25.517241381
2.4%
25.468164791
2.4%
25.352112681
2.4%
24.715909091
2.4%
24.637681161
2.4%
24.406779661
2.4%

REMP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean18.33878721
Minimum12.70833333
Maximum24.41314554
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:45.783121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum12.70833333
5-th percentile13.31398437
Q115.80730761
median18.125
Q320.90108401
95-th percentile23.308662
Maximum24.41314554
Range11.70481221
Interquartile range (IQR)5.093776397

Descriptive statistics

Standard deviation3.286733297
Coefficient of variation (CV)0.1792230456
Kurtosis-0.9236437444
Mean18.33878721
Median Absolute Deviation (MAD)2.717572062
Skewness0.03775487097
Sum568.5024036
Variance10.80261576
MonotonicityNot monotonic
2022-11-25T20:46:45.943119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
17.897091721
 
2.4%
20.842572061
 
2.4%
17.866666671
 
2.4%
23.59249331
 
2.4%
14.576271191
 
2.4%
19.417475731
 
2.4%
12.753623191
 
2.4%
23.004694841
 
2.4%
16.666666671
 
2.4%
20.959595961
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
12.708333331
2.4%
12.753623191
2.4%
13.874345551
2.4%
14.204545451
2.4%
14.576271191
2.4%
14.754098361
2.4%
14.973262031
2.4%
15.652173911
2.4%
15.962441311
2.4%
16.206896551
2.4%
ValueCountFrequency (%)
24.413145541
2.4%
23.59249331
2.4%
23.02483071
2.4%
23.004694841
2.4%
22.51
2.4%
21.028037381
2.4%
21.022727271
2.4%
20.959595961
2.4%
20.842572061
2.4%
20.698254361
2.4%

SMI
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct31
Distinct (%)100.0%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean85.63412094
Minimum69.98011928
Maximum91.20521173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:46.107118image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum69.98011928
5-th percentile81.21953518
Q184.11970407
median86.1423221
Q387.96929825
95-th percentile90.0653239
Maximum91.20521173
Range21.22509244
Interquartile range (IQR)3.849594178

Descriptive statistics

Standard deviation3.953232048
Coefficient of variation (CV)0.0461642159
Kurtosis7.29108545
Mean85.63412094
Median Absolute Deviation (MAD)1.857677903
Skewness-2.099746075
Sum2654.657749
Variance15.62804362
MonotonicityNot monotonic
2022-11-25T20:46:46.275158image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
89.221556891
 
2.4%
88.779527561
 
2.4%
88.235294121
 
2.4%
88.598574821
 
2.4%
82.865168541
 
2.4%
87.473460721
 
2.4%
80.985915491
 
2.4%
88.565488571
 
2.4%
86.896551721
 
2.4%
85.714285711
 
2.4%
Other values (21)21
50.0%
(Missing)11
26.2%
ValueCountFrequency (%)
69.980119281
2.4%
80.985915491
2.4%
81.453154881
2.4%
81.696428571
2.4%
82.149712091
2.4%
82.765151521
2.4%
82.865168541
2.4%
83.81502891
2.4%
84.424379231
2.4%
84.703632891
2.4%
ValueCountFrequency (%)
91.205211731
2.4%
90.909090911
2.4%
89.221556891
2.4%
88.779527561
2.4%
88.598574821
2.4%
88.565488571
2.4%
88.235294121
2.4%
881
2.4%
87.938596491
2.4%
87.912087911
2.4%

Midpoint
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct30
Distinct (%)96.8%
Missing11
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean4.202419355
Minimum3.358333333
Maximum5.625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:46.432117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.358333333
5-th percentile3.508333333
Q13.8625
median4.175
Q34.4125
95-th percentile5.008333333
Maximum5.625
Range2.266666667
Interquartile range (IQR)0.55

Descriptive statistics

Standard deviation0.5117471596
Coefficient of variation (CV)0.1217744152
Kurtosis0.6817160293
Mean4.202419355
Median Absolute Deviation (MAD)0.3
Skewness0.763271739
Sum130.275
Variance0.2618851553
MonotonicityNot monotonic
2022-11-25T20:46:46.589119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.42
 
4.8%
4.1751
 
2.4%
4.2333333331
 
2.4%
3.5916666671
 
2.4%
3.8751
 
2.4%
4.9666666671
 
2.4%
3.9251
 
2.4%
4.1166666671
 
2.4%
4.0083333331
 
2.4%
4.8333333331
 
2.4%
Other values (20)20
47.6%
(Missing)11
26.2%
ValueCountFrequency (%)
3.3583333331
2.4%
3.4251
2.4%
3.5916666671
2.4%
3.6916666671
2.4%
3.7166666671
2.4%
3.7333333331
2.4%
3.7751
2.4%
3.851
2.4%
3.8751
2.4%
3.9166666671
2.4%
ValueCountFrequency (%)
5.6251
2.4%
5.0333333331
2.4%
4.9833333331
2.4%
4.9666666671
2.4%
4.8333333331
2.4%
4.5751
2.4%
4.551
2.4%
4.4251
2.4%
4.42
4.8%
4.3583333331
2.4%

Day
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size464.0 B
Friday
Saturday
Sunday
Monday
Tuesday
Other values (2)
12 

Length

Max length9
Median length7
Mean length7.142857143
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFriday
2nd rowSaturday
3rd rowSunday
4th rowMonday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Friday6
14.3%
Saturday6
14.3%
Sunday6
14.3%
Monday6
14.3%
Tuesday6
14.3%
Wednesday6
14.3%
Thursday6
14.3%

Length

2022-11-25T20:46:46.772119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-25T20:46:46.889153image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
friday6
14.3%
saturday6
14.3%
sunday6
14.3%
monday6
14.3%
tuesday6
14.3%
wednesday6
14.3%
thursday6
14.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

IsWeekend
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size170.0 B
False
30 
True
12 
ValueCountFrequency (%)
False30
71.4%
True12
 
28.6%
2022-11-25T20:46:46.978150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

SleepRegularity
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)94.4%
Missing6
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean0.4382580288
Minimum0.1736722115
Maximum0.7415340081
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2022-11-25T20:46:47.081136image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.1736722115
5-th percentile0.2414205963
Q10.3151841051
median0.4031526401
Q30.529758046
95-th percentile0.7307920352
Maximum0.7415340081
Range0.5678617966
Interquartile range (IQR)0.2145739409

Descriptive statistics

Standard deviation0.164194999
Coefficient of variation (CV)0.3746537158
Kurtosis-0.8112915236
Mean0.4382580288
Median Absolute Deviation (MAD)0.1170792663
Skewness0.4655577273
Sum15.77728904
Variance0.02695999768
MonotonicityNot monotonic
2022-11-25T20:46:47.246150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0.39703764172
 
4.8%
0.25934836432
 
4.8%
0.74153400811
 
2.4%
0.67693481281
 
2.4%
0.71851229681
 
2.4%
0.72924772791
 
2.4%
0.73542495721
 
2.4%
0.61189433941
 
2.4%
0.64072286811
 
2.4%
0.40926763861
 
2.4%
Other values (24)24
57.1%
(Missing)6
 
14.3%
ValueCountFrequency (%)
0.17367221151
2.4%
0.19056142691
2.4%
0.25837365281
2.4%
0.25934836432
4.8%
0.26273137951
2.4%
0.28500324881
2.4%
0.28714349881
2.4%
0.30320052041
2.4%
0.31917863341
2.4%
0.32217432591
2.4%
ValueCountFrequency (%)
0.74153400811
2.4%
0.73542495721
2.4%
0.72924772791
2.4%
0.71851229681
2.4%
0.67693481281
2.4%
0.65752587611
2.4%
0.64072286811
2.4%
0.61189433941
2.4%
0.53575837561
2.4%
0.52775793611
2.4%

Interactions

2022-11-25T20:46:36.785135image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:45:53.873962image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:45:56.816959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:45:59.710953image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:02.788954image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:05.655990image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:08.312992image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:31.012044image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:02.137967image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:04.822953image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:10.495954image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:13.582105image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:19.162715image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:21.473710image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:24.037708image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:28.664039image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:33.667041image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:38.726166image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:45:56.326956image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:45:59.234959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:02.313959image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:07.839956image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:16.611285image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:19.299708image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:21.614710image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:24.161738image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:28.787041image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:31.301045image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-25T20:46:34.135038image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-25T20:46:36.633201image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2022-11-25T20:46:47.420161image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-25T20:46:47.712117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-25T20:46:48.004158image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-25T20:46:48.265121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-25T20:46:48.438121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-25T20:46:39.264149image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-25T20:46:39.862121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-25T20:46:40.233117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-25T20:46:40.598150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexOnsetOffsetTSTWASONOATIBREMSDLSDDSDDateTSDPAISWSPREMPSMIMidpointDayIsWeekendSleepRegularity
0023.1833337.533333447.054.033.0501.080.0277.090.02022-09-30501.04.42953020.13422817.89709289.2215574.175000FridayTrueNaN
1122.5500007.350000437.091.032.0528.084.0297.056.02022-10-01528.04.39359312.81464519.22196882.7651524.400000SaturdayTrueNaN
2222.7500006.866667426.061.028.0487.0104.0264.058.02022-10-02487.03.94366213.61502324.41314687.4743334.058333SundayFalse0.173672
3323.1500006.583333382.064.028.0446.053.0247.082.02022-10-03446.04.39790621.46596913.87434685.6502243.716667MondayFalse0.285003
4524.5833337.250000352.048.020.0400.074.0208.070.02022-10-04400.03.40909119.88636421.02272788.0000003.916667TuesdayFalse0.258374
5422.4333337.116667428.093.025.0521.090.0236.0102.02022-10-05521.03.50467323.83177621.02803782.1497124.341667WednesdayFalse0.259348
631NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-06NaNNaNNaNNaNNaNNaNThursdayFalse0.259348
732NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-07NaNNaNNaNNaNNaNNaNFridayTrue0.287143
833NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-08NaNNaNNaNNaNNaNNaNSaturdayTrue0.262731
934NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-10-09NaNNaNNaNNaNNaNNaNSundayFalse0.319179

Last rows

df_indexOnsetOffsetTSTWASONOATIBREMSDLSDDSDDateTSDPAISWSPREMPSMIMidpointDayIsWeekendSleepRegularity
322323.0833338.750000504.076.040.0580.084.0323.097.02022-11-01580.04.76190519.24603216.66666786.8965524.833333TuesdayFalse0.735425
332423.7166677.733333426.055.033.0481.098.0263.065.02022-11-02481.04.64788715.25821623.00469588.5654894.008333WednesdayFalse0.611894
342624.5666677.666667345.081.027.0426.044.0216.085.02022-11-03426.04.69565224.63768112.75362380.9859154.116667ThursdayFalse0.640723
352523.4333337.283333412.059.031.0471.080.0260.072.02022-11-04471.04.51456317.47572819.41747687.4734613.925000FridayTrue0.464290
362726.0000007.933333295.061.024.0356.043.0180.072.02022-11-05356.04.88135624.40678014.57627182.8651694.966667SaturdayTrue0.527758
3740NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-11-06NaNNaNNaNNaNNaNNaNSundayFalse0.487653
3841NaNNaNNaNNaNNaNNaNNaNNaNNaN2022-11-07NaNNaNNaNNaNNaNNaNMondayFalse0.490840
392824.3666677.383333373.048.028.0421.088.0217.068.02022-11-08421.04.50402118.23056323.59249388.5985753.875000TuesdayFalse0.450100
402924.0500007.133333375.050.024.0425.067.0218.090.02022-11-09425.03.84000024.00000017.86666788.2352943.591667WednesdayFalse0.522235
413026.1000007.866667290.056.017.0346.047.0169.074.02022-11-10346.03.51724125.51724116.20689783.8150294.983333ThursdayFalse0.657526